Print Article - Engle, = Granger win Nobel Prize for Economics

From: Subject: Print Article - Engle, Granger win Nobel Prize for Economics Date: Thu, 9 Oct 2003 15:31:12 -0500 MIME-Version: 1.0 Content-Type: multipart/related; boundary="----=_NextPart_000_0000_01C38E7A.5EC92260"; type="text/html" X-MimeOLE: Produced By Microsoft MimeOLE V6.00.2800.1165 This is a multi-part message in MIME format. ------=_NextPart_000_0000_01C38E7A.5EC92260 Content-Type: text/html; charset="utf-8" Content-Transfer-Encoding: quoted-printable Content-Location: http://ushome.rediff.com/cms/print.jsp?docpath=/money/2003/oct/08nobel.htm =EF=BB=BF Print Article - Engle, = Granger win Nobel Prize for Economics

Engle,=20 Granger win Nobel Prize for Economics

Agencies | AGENCIES | October 08, 2003 | 20:46=20 IST

3D"Robert American Robert F Engle and Briton Clive W J = Granger on=20 Wednesday won the 2003 Nobel Economics Prize for their work in = analysing=20 economic time series.

The Royal Swedish Academy of Sciences decided that the Bank of = Sweden=20 Prize in Economic Sciences in Memory of Alfred Nobel, 2003, would = be=20 shared between the two 'for methods of analyzing economic time = series with=20 time-varying volatility' and 'for methods of analysing economic = time=20 series with common trends (cointegration).'

Robert Engle was = born in=20 1942 in Syracuse, NY, USA (American citizen) and got his Ph.D. = from=20 Cornell University in 1969.=20

He is Michael Armellino Professor of Management of Financial = Services=20 at New York University, NY, USA.

3D"Clive Clive W J Granger was born in 1934 in Swansea, = Wales=20 (British citizen) and obtained his Ph.D. from University of = Nottingham in=20 1959.

He is emeritus Professor of Economics at University of = California at=20 San Diego, USA.

Their research gave economists new tools to evaluate risk by = improving=20 analysis of indicators such as economic growth, prices or interest = rates=20 over a period of time.

The prize money of 10 million kronor ($1.3 million) will be = divided=20 equally between them.

Statistical methods for economic time = series

Researchers use data in the form of time series, i.e., = chronological=20 sequences of observations, when estimating relationships and = testing=20 hypotheses from economic theory. Such time series show the = development of=20 GDP, prices, interest rates, stock prices, etc.

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During the 1980s, this year's Laureates devised new statistical = methods=20 for dealing with two key properties of many economic time series:=20 time-varying volatility and nonstationarity.

On financial markets, random fluctuations over time - = volatility - are=20 particularly significant because the value of shares, options and = other=20 financial instruments depends on their risk.

Fluctuations can vary considerably over time; turbulent periods = with=20 large fluctuations are followed by calmer periods with small=20 fluctuations.

Despite such time-varying volatility, in want of a better = alternative,=20 researchers used to work with statistical methods that presuppose = constant=20 volatility.

Robert Engle's discovery was therefore a major breakthrough. He = found=20 that the concept of autoregressive conditional heteroskedasticity = (ARCH)=20 accurately captures the properties of many time series and = developed=20 methods for statistical modeling of time-varying volatility.

His ARCH models have become indispensable tools not only for=20 researchers, but also for analysts on financial markets, who use = them in=20 asset pricing and in evaluating portfolio risk.

Most macroeconomic time series follow a stochastic trend, so = that a=20 temporary disturbance in, say, GDP has a long-lasting effect.

These time series are called nonstationary; they differ from = stationary=20 series which do not grow over time, but fluctuate around a given=20 value.

Clive Granger demonstrated that the statistical methods used = for=20 stationary time series could yield wholly misleading results when = applied=20 to the analysis of nonstationary data.

His significant discovery was that specific combinations of=20 nonstationary time series may exhibit stationarity, thereby = allowing for=20 correct statistical inference.

Granger called this phenomenon cointegration. He developed = methods that=20 have become invaluable in systems where short-run dynamics are = affected by=20 large random disturbances and long-run dynamics are restricted by = economic=20 equilibrium relationships.

Examples include the relations between wealth and consumption, = exchange=20 rates and price levels, and short and long-term interest=20 rates.

URL for this=20 = article:
http://www.rediff.com//money/2003/oct/08nobel.htm

=C2=A9 2003 rediff.com India Limited. = All Rights=20 Reserved. This material may not be published, broadcast, = rewritten, or=20 = redistributed.

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